The AI Product Mandate

Every few decades, product management gets rewritten. Agile redefined how teams ship. Cloud redefined how they scale. Mobile redefined how they reach customers. Now AI is forcing a reset at every layer: from how you scope roadmaps to how you measure value, from how you hire to how your products are consumed. The problem isn’t that executives “don’t get AI.” The problem is they’re asking the wrong questions. Too many still obsess over:

• Which model should we use?
• How accurate is it?
• What’s the context window?

Those questions are already stale.

The sharper question is: How do we build AI products that solve painful, high-margin problems, deliver measurable outcomes, and survive in a world where our buyers and teammates are increasingly non-human?

I’ve spent the past 3 decades advising federal leaders, Fortune-scale operators, and startup founders navigating exactly this reset. Along the way, I’ve written about compound AI systems, agent-first design, cognitive browsing, business-to-agent commerce, and the sandbox economies forming between machines.

Today, I want to compress those threads into eight imperatives that separate AI-native product teams from everyone else. Imperatives that go to the harder truth:

AI is not a feature, not the product, but a volatile, compounding capability you must design into your operating DNA.

Section 1 – From Features to Capabilities

We’ve passed the novelty phase. AI is not a gimmick to sprinkle onto roadmaps. It is a capability substrate that rewrites what’s possible across the stack.

• In A Blueprint for Building AI, I argued that orchestration—how you combine models, tools, and humans—is more decisive than any single model’s benchmark.
• In Your Next Hire Has a GPU, I showed how autonomous engineers like Devin (from Cognition acquired by Windsurf ) change the very shape of software teams.
• In Build for the Buyer That Never Blinks, I mapped how your decisive customer will soon be an agent parsing your schema, not a human browsing your website.

Signals are converging:

• Agents are crossing from copilots to operators. They’re not just suggesting drafts; they’re executing workflows, fixing bugs, and reconciling invoices.
• Interfaces are collapsing into workflows. In Cognitive Browsing, I showed how the browser is becoming a cognitive OS. Tabs give way to actions. That same collapse is happening across enterprise stacks.
• Commerce is going agent-native. Payment rails (Visa, Mastercard, PayPal) now publish protocols for agent-to-agent settlement. Marketplaces like Amazon’s Rufus route discovery inside AI assistants.

The bottleneck has shifted. It’s no longer about model IQ. It’s about integration, orchestration, trust, and velocity.

Section 2 – Challenge the Default Thinking

Most organizations still approach AI with reflexes shaped by the last era of software. Those defaults are already failing.

• Default reflex 1: Treat AI like a product.
• Reality: AI is a capability that must be fused with a business problem worth solving.
• Default reflex 2: Measure success by accuracy.
• Reality: Accuracy without business lift is theater.
• Default reflex 3: Assume “done” means shipped.
• Reality: In AI, “done” means continuously learning, drifting, retraining.

The hard truth: most AI roadmaps are bloated with shiny experiments that never connect to outcomes.

The contrarian move isn’t to ship more AI. It’s to ship less AI, more deliberately. Ruthlessly tie every initiative to a KPI your CFO cares about. Treat drift, bias, and governance as design surfaces, not compliance afterthoughts.

Section 3 – The Eight Imperatives

1. Treat AI as Capability

AI is not your product. Your product solves a user or business pain, and AI is one of the levers.

If you can solve the problem with a simpler method then do that. Hardcode first. Prototype manually. Validate fit. Then apply AI where it creates 10x or more leverage.

That’s how winning teams avoid the trap of “AI for AI’s sake.” In government benefits, I’ve seen case teams build manual adjudication flows first, then layer AI triage only once the flow was proven. In retail, personalization features that once felt like magic now prove their worth only when tied directly to revenue lift.

This is the essence of Blueprint for Building AI: build scaffolding, validate outcomes, then orchestrate intelligence.

2. Redefine Success Metrics

Accuracy is not the KPI. Business impact is.

The right question isn’t “Is the model 92% accurate?” It’s “Did case resolution time drop by 30%?” “Did retention increase by 5 points?” “Did churn reduce enough to offset model costs?”

In Jagged Intelligence, I described how AI capability is spiky. You win by choreographing those spikes against metrics that matter. That means designing A/B tests with and without the AI feature and measuring lift in behavior, not just prediction quality.

Acceptance criteria must evolve too. In classical software, “done” means feature complete.

In AI, “done” means “predictive quality sufficient to move a KPI.”

3. Build the AI Product Lifecycle Into Your Org

AI isn’t a project. It’s a loop.

• Discovery: Identify user pain or business inefficiency.
• Feasibility: Assess data readiness and model potential.
• Prototype: Test fast, cheap, and ugly.
• Validation: Does it change behavior or outcomes?
• Iteration: Improve model, data, and UX.
• Deployment: Scale with guardrails.
• Monitoring: Track drift, bias, and impact.

This loop mirrors the conversational cycles I explored in Conversation Engineering. Cold starts are inevitable—design onboarding strategies, feedback loops, and data collection early. A fraud detection system with no feedback is just guessing. A fraud system with structured dispute loops learns daily.

4. Data Is the New Spec

In an AI-native product, your PRD (product requirements document) doesn’t describe features. It describes data flows.

• What data do we have?
• What’s missing?
• What’s biased?
• What’s fresh?
• What’s decayed?

In Your Dev Team Is Already Obsolete, I argued that context is the new source code. The same applies here. Your competitive moat isn’t lines of code; it’s the structured context that feeds your models.

PMs must become data literate. If you can’t interrogate data pipelines, you can’t manage AI products.

5. Bake in Risk and Ethics From Day One

Bias, explainability, unintended consequences—these are design constraints, not afterthoughts.

In Securing the Agent Surface, I showed how context itself becomes capability. A poisoned tool description can become an exploit. The same applies to business AI: a biased dataset isn’t just inaccurate, it’s a liability.

Adopt frameworks like consequence scanning and bias monitoring. Use explainability as a requirement, especially in regulated sectors. And be transparent not just about what the system does, but why.

6. Embrace Adaptability: The Only Constant Is Drift

AI systems are probabilistic. Drift is guaranteed.

The winning mindset isn’t “How do we stop drift?” but “How do we detect and harness drift faster than competitors?”

In The Age of Self-Improving Software, I showed how self-play and feedback loops can make drift a source of improvement. The same is true in enterprise AI.

Regular experimentation, A/B testing, and retraining are survival skills.

Organizations that routinize experiments develop “AI intuition”—a cultural muscle memory that compounds over time.

7. Upskill and Restructure Your Team

New roles are emerging:

• AI Systems Architects – design agent workflows and guardrails
• Data Curators – steward the provenance, quality, and freshness of data
• AI QA Specialists – build test harnesses to catch drift and enforce fairness

In Your Next Hire Has a GPU, I argued your next teammate may be an agent. That shifts human roles. Developers become orchestrators, curators, and governors of hybrid teams. Product managers become translators between business needs and AI constraints.

Teams that fail to re-skill will find themselves outpaced not by better models, but by better organized competitors.

8. Prepare for Human + AI Symbiosis

AI is not replacing your people. It’s reshaping their edge.

In Jagged Intelligence, I argued the future belongs to orchestrators.

Winning teams harness jagged symbiosis. They delegate spikes of machine capability—superhuman code search, endless log parsing—while covering valleys of machine naiveté: ethics, context, values.

This symbiosis is not optional. It is the new organizational muscle.

Section 4 – Business Implications

Enterprises

Tie every AI initiative to ROI. Redefine evaluation: the question isn’t “Does this feature work?” but “Does this workflow compound value?” Build dashboards that tie model performance directly to revenue, churn, or satisfaction.

Startups

Velocity is your moat. Adopt routing layers. Build agent-first storefronts. Default to small, specialized models where possible. Capital efficiency wins.

Public Sector

AI in government must be auditable, attributable, and reversible. In Sandbox Economy, I warned that emergent, permeable agent markets carry systemic risks. For public missions, design proofs, policies, and explainability into the stack from day one.

All Sectors

Your decisive buyer will soon be an agent. That means your brand is no longer your pixels—it’s your schema, your proofs, your latency.

Section 5 – Playbook

1. Audit your roadmap – kill AI projects not tied to KPIs.
2. Stand up a data-first culture – squads own and improve their flows.
3. Mandate ethical checkpoints – prototype, pre-launch, post-launch.
4. Host AI demo days – celebrate wins tied to business metrics.
5. Weaponize dashboards – connect AI performance directly to bottom-line impact.

Section 6 – From Features to Agent Economies

The frontier is orchestration, protocols, and markets.

• In Sandbox Economy, I showed how agents will transact in semi-permeable markets.
• In Business to Agents, I argued that brands become schemas, not stories.
• In Route, Don’t Guess, I explained how routing cognition is the economic spine of AI-native systems.

The future: your products will be consumed, evaluated, and even purchased by agents. Humans will still matter for budgets, values, and risk, but the daily surface of software will be agentic.

That flips the builder’s mandate. Stop bolting AI onto human-first design. Start making your business legible, trustworthy, and efficient to machines while keeping humans in the loop for governance and ethics.

Closing Thoughts

AI is not a feature. It is not the product. It is the most volatile, compounding capability in the modern stack.

Your advantage is your velocity, your rigor, your ethics, and your ability to connect prediction to outcomes.

The organizations that thrive will be those who:

• Treat AI as capability
• Obsess over outcomes
• Routinize adaptation and drift
• Re-skill teams for orchestration
• Design for human + AI symbiosis

Question for you: If an AI agent evaluated your product tomorrow by reading your schema, testing your latency, and checking your proofs, would it choose you?